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相关概念视频

Imaging Studies for Cardiovascular System II:Types of Echocardiography01:20

Imaging Studies for Cardiovascular System II:Types of Echocardiography

272
Echocardiography plays a role in assessing cardiac health and detecting heart conditions, with various types providing critical insights for diagnosis and treatment.
Types of Echocardiography
Transthoracic Echocardiography (TTE)
TTE is the most common type of echocardiogram which involves placing a transducer on the patient's chest, emitting sound waves to create heart images. TTE is invaluable for evaluating the heart's size, structure, and motion, making it particularly useful for...
272
Imaging Studies for Cardiovascular System I:Echocardiography01:17

Imaging Studies for Cardiovascular System I:Echocardiography

335
Cardiac imaging studies encompass a wide range of noninvasive and minimally invasive techniques designed to visualize the heart's structure and function in detail. One such technique is echocardiography, which uses high-frequency ultrasound waves to produce detailed images of the heart, known as echocardiograms.
Indications: Echocardiography is utilized to diagnose heart failure, valve disorders, and myocardial infarction. It also assesses cardiac structures' size, shape, and motion,...
335

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相关实验视频

Updated: Jul 6, 2025

Transthoracic Echocardiography in Mice
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深度学习用于经食道心声学视图分类的深度学习.

Kirsten R Steffner1, Matthew Christensen2, George Gill3

  • 1Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University, 300 Pasteur Drive, Stanford, CA, 94305, USA. ksteffner@stanford.edu.

Scientific reports
|January 3, 2024
PubMed
概括

一个新的深度学习模型准确地分类了跨食道心声学 (TEE) 视图. 这种人工智能工具结构复杂的心脏成像数据,使高级分析能够在手术期间更好地照顾患者.

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相关实验视频

Last Updated: Jul 6, 2025

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08:09

Transthoracic Echocardiography in Mice

Published on: May 28, 2010

62.2K
Transthoracic Echocardiographic Examination in the Rabbit Model
14:46

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Published on: June 1, 2019

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科学领域:

  • 心脏病学 心脏病学
  • 医疗成像医学成像
  • 人工智能的人工智能

背景情况:

  • 过食管回声心脏图 (TEE) 对于评估心脏病状况和指导心脏手术至关重要.
  • 在利用TEE数据进行深度学习时,一个重大挑战是图像的固有复杂性和非结构性.
  • 对TEE视图的标准化分类对于统一的数据分析和解释至关重要.

研究的目的:

  • 开发和验证基于深度学习的模型,用于标准化TEE视图的多类别分类.
  • 在手术内和手术内TEE成像数据中引入结构.
  • 为了促进下游的深度学习应用程序TEE数据.

主要方法:

  • 一个卷积神经网络 (CNN) 被训练来预测标准化的TEE视图.
  • 该模型使用Cedars-Sinai医疗中心 (CSMC) 标记的手术内和手术内TEE视频进行训练.
  • 外部验证是在斯坦福大学医学中心 (SUMC) 的手术内TEE视频上进行的.

主要成果:

  • 深度学习模型在对所有标记TEE视图进行分类时表现出高准确度.
  • 超胃左心室短轴视图 (AUC 0.971 在CSMC, 0.957 在SUMC) 的表现最高.
  • 其他高度准确的分类包括食道中长轴视图,食道中大动脉短轴视图和食道中4室视图.

结论:

  • 开发的深度学习模型准确地分类了标准化的TEE视图.
  • 这种分类能力增强了TEE成像用于深度学习分析的实用性.
  • 该模型为手术内和手术内TEE数据提供了一个结构化的方法,为先进的AI驱动的洞察力铺平了道路.